Recent Progress on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Ge, Kun; Hu, Yuling; Li, Gongke

doi:10.3390/bios12110941

Cited by 23 publications

(9 citation statements)

References 138 publications

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“…However, conventional colloidal-based SERS substrates are not found to be ideal due to the different stages and efforts required to concentrate and aggregate the deposited nanoparticles on the solid surface substrate. Consequently, solid SERS substrates (paper membrane, silicone, polymer, glass, chip, or fiber-based) with advantages including dense hot spot, stability, desired morphology, and portability have been developed . For example, a filter paper-based nanoparticle substrate was employed to detect pesticides in a portable background, i.e., aqueous and solids, and was found to be cost-effective, reproducible, and sensitive …”

Section: Overview Of Raman Spectroscopy and Sers For Pesticide Detectionmentioning

confidence: 99%

“…Consequently, solid SERS substrates (paper membrane, silicone, polymer, glass, chip, or fiber-based) with advantages including dense hot spot, stability, desired morphology, and portability have been developed. 31 For example, a filter paper-based nanoparticle substrate was employed to detect pesticides in a portable background, i.e., aqueous and solids, and was found to be cost-effective, reproducible, and sensitive. 32 2.3.2.…”

Section: Development and Utilization Of Sers Techniques For Different...mentioning

confidence: 99%

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Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Sharma,

Kolašinac,

Jiang

et al. 2024

ACS Agric. Sci. Technol.

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Inappropriate pesticide usage leads to unsustainable agricultural practices and deteriorates the quality of fruits and vegetables by introducing potentially hazardous substances. Raman spectroscopy, specifically surface-enhanced Raman spectroscopy (SERS), offers high-sensitivity in situ monitoring of pesticide residues. This review emphasizes the importance of advanced databases and algorithms in interpreting Raman signals. Various statistical models are introduced for spectral analysis, including self-modeling curve resolution, multivariate curve resolution, and self-modeling mixture analysis. Additionally, this study provides comprehensive information on different SERS substrates and shows great potential in the determination of food pesticide residues. However, a multicomponent analysis is needed for pesticide mixtures. The overlapping of the bands needs to be considered due to the complex matrices of biological samples. Artificial neural networks (ANNs) are applied as nonlinear models when the analytes are in a multicomponent mixture. Further research is needed to establish standardized protocols for SERS-based pesticide quantitative detection, including sample preparation and data analysis.

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Section: Overview Of Raman Spectroscopy and Sers For Pesticide Detectionmentioning

confidence: 99%

Section: Development and Utilization Of Sers Techniques For Different...mentioning

confidence: 99%

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Sharma,

Kolašinac,

Jiang

et al. 2024

ACS Agric. Sci. Technol.

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“…The preparation of nanocolloid substrates is much simpler with respect to the solid SERS substrate, with the ability to ignore the interference of water signals and inhomogeneous signals captured from SERS measurement using SERS substrate. However, liquid substrates have many limitations, including difficult long-term storage because of the easy aggregation of nanoparticle colloids, lower SERS activity, and unstable signals in terms of the inherent instability of nanoparticles [ 47 ]. According to the report, non-aggregated colloidal substrate only reaches the 10 4 –10 5 order of magnitude of the SERS analytical enhancement factor (AEF) [ 48 ].…”

Section: Sers-based Nanobiosensorsmentioning

confidence: 99%

SERS-Based Optical Nanobiosensors for the Detection of Alzheimer’s Disease

Gao,

Li,

Wang

et al. 2023

Biosensors

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Alzheimer’s disease (AD) is a leading cause of dementia, impacting millions worldwide. However, its complex neuropathologic features and heterogeneous pathophysiology present significant challenges for diagnosis and treatment. To address the urgent need for early AD diagnosis, this review focuses on surface-enhanced Raman scattering (SERS)-based biosensors, leveraging the excellent optical properties of nanomaterials to enhance detection performance. These highly sensitive and noninvasive biosensors offer opportunities for biomarker-driven clinical diagnostics and precision medicine. The review highlights various types of SERS-based biosensors targeting AD biomarkers, discussing their potential applications and contributions to AD diagnosis. Specific details about nanomaterials and targeted AD biomarkers are provided. Furthermore, the future research directions and challenges for improving AD marker detection using SERS sensors are outlined.

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“…Therefore, SERS technique has been pointed out as one of the most promising alternatives for POPs detection [28,[33][34][35][36][37][38][39][40][41][42][43][44], mainly due to the very low detection limits obtained [45], limits unreachable with traditional detection procedures based on bioassays or chromatography techniques. Another advantage of spectroscopic techniques in general and SERS in particular is the lower costs compared to bioassays and gas chromatography [15]; costs that have been reduced gradually by improving the fabrication techniques of SERS substrates or replacing the traditional gold and silver nanoparticles by less expensive nanostructured materials [55][56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Simulating Detection of Dioxin-like Pollutants with 2D Surface Enhanced Raman Spectroscopy using h-BNC Substrates

Valencia-Alvarado¹,

Otero²,

Mandado³

et al. 2023

Preprint

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The ability of 2D hybrid structures formed by boron, nitrogen and carbon atoms (h-BNCs) as potential substrates for surface enhanced Raman spectroscopy (SERS) detection of dioxin-like pollutants is theoretically analyzed. The strong confinement and high tunability of the electromagnetic response of the carbon nanostructures embedded within the h-BNC sheets point out that these hybrid structures could be promising for applications in optical spectroscopies, such as SERS. In this work, two model dioxin-like pollutants, TCDD and TCDF, and a model h-BNC surface composed by a carbon nanodisk of ninety-six atoms surrounded by a string of borazine rings, BNC96, are employed for the simulation of the adsorption complexes and the static and pre-resonance Raman spectra of the adsorbed molecules. A large affinity of BNC96 by these pollutants is reflected on the large interaction energies obtained for the most stable stacking complexes, with dispersion being the most important contribution to their stability. The large vibrational coupling of some active modes of TCDF and, specially, of TCDD makes the Raman static spectra display a ‘pure’ chemical enhancement of one order of magnitude. On the other hand, due to the strong electromagnetic response of BNC96, confined within the carbon nanodisk, pre-resonance Raman spectra obtained for TCDD and TCDF display large enhancement factors of 108 and 107, respectively. Promisingly, laser excitation wavelengths frequently used in SERS experiments also provoke significant Raman enhancements, around 104, for the TCDD and TCDF signals. Both the strong confinement of the electromagnetic response within the carbon domains and the high modulation of the resonance wavelengths within the visible and/or UV ranges in h-BNCs should lead to a higher sensitivity than graphene and white graphene parent structures, solving one of the main disadvantages of using 2D substrates for SERS applications.

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Recent Progress on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Cited by 23 publications

References 138 publications

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

Raman Spectroscopy-Based Chemometrics for Pesticide Residue Detection: Current Approaches and Future Challenges

SERS-Based Optical Nanobiosensors for the Detection of Alzheimer’s Disease

Simulating Detection of Dioxin-like Pollutants with 2D Surface Enhanced Raman Spectroscopy using h-BNC Substrates

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